A study on video viewing behavior: application to movie trailer miner
A study on video viewing behavior: application to movie trailer miner
复制标题
DOI：
10.1080/17445760601125376
复制
发表时间：
2007-01-01
影响因子：
1.1
通讯作者：
Mongy, Sylvain
中科院分区：
文献类型：
--
作者：
Mongy, Sylvain
在本文中，我们提出了一个关于视频观看行为的研究。在一个非常适合的马尔可夫模型的基础上，我们开发了一种称为K-Models的聚类算法，并受到K-Means技术的启发来对行为进行聚类和分析。这些模型是使用在用户观看视频序列时向其建议的不同动作(播放、暂停、前进、倒带、跳转、停止)来构建的。我们已经用一个电影预告片挖掘工具应用了我们的算法。该工具允许用户根据基本属性(演员、导演、银幕日期...)执行搜索并观看精选的预告片。有了合适的服务器，我们会记录每一次操作以分析行为。首先，从一组测试版用户获得的结果揭示了有趣的典型行为。
In this paper, we present a study on video viewing behavior. Based on a well-suited Markovian model, we have developed a clustering algorithm called K-models and inspired by the K-means technique to cluster and analyze behaviors. These models are constructed using the different actions proposed to the user while he is viewing a video sequence (play, pause, forward, rewind, jump, stop). We have applied our algorithm with a movie trailer mining tool. This tool allows users to perform searches on basic attributes (cast, director, onscreen date...) and to watch selected trailers. With an appropriate server, we log every action to analyze behaviors. First results obtained from a set of beta users reveal interesting typical behaviors.